Inertial Measurement Units (IMUs) typically use accelerometers and gyros to track accelerations in order to calculate changes in position. Because of inherent errors in the sensors used in IMUs, random errors in the calculated position build with time. One current method of eliminating these errors is to use a second position sensor (e.g., a GPS or an odometer input) along with a Kalman filter to minimize errors from each of the position sensors. A second method is to stop and perform a Zero Velocity Update (ZVU) to re-calibrate the sensors at the beginning of a period of interest.
Most applications using IMUs require good absolute position accuracy over long distances, such as for use in a plane or a tank. Such applications are ideal for use of an IMU coupled with a secondary data source such as a Global Positioning System (GPS) to bound and correct errors in the IMU. A typical IMU operating in conjunction with the GPS may be capable of 10 meters in accuracy relative to a fixed reference point after traversing a distance of 100 miles.
In contrast, some applications, such as to which this invention applies, requires extremely accurate relative position data over short distances. In such applications, it is necessary to determine with high accuracy how far the IMU has moved since the last ZVU was performed.
Neither of the two methods described above are suitable when very high relative positional accuracy is required over very short distances. For example, where a vehicle is used to detect and destroy a land mine, 3 centimeters of accuracy in 6 meters of travel is required. These high levels of accuracy preclude the use of coarse sensors such as an odometer or GPS to eliminate the errors. The nature of these applications also prevents a ZVU from being performed at the beginning of each period of interest (i.e., sensing of a mine or false alarm). Other solutions to the problem of accumulation of errors from an IMU include the use of more expensive sensors with lower noise, and using additional external equipment. As an example, survey quality differential Real Time Kinematic (RTK) GPS can provide highly accurate positional data with the use of a ground based station at a known point, which is disadvantageous in some situations. Thus, to achieve the requisite high levels of accuracy would generally require an IMU using extremely accurate sensors costing approximately $100,000. Therefore, there is a need for methods and apparatus for generating highly accurate position data over short periods of travel using a less expensive IMU.